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Rational design and glass-forming ability prediction of bulk metallic glasses via interpretable machine learning

  • Computation & theory
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Abstract

The prediction accuracy of current mainstream machine learning (ML) models depends on regulating many hyperparameters. In this paper, a deep forest (DF) model with a few hyperparameters and a non-excessive dependence on super parameter regulation was applied to the prediction of glass-forming ability (GFA) of bulk metallic glasses (BMGs). Compared with these of the mainstream ML models, including Support Vector Regression (SVR), random forest (RF), gradient boosted decision trees (GBDT), k-nearest neighbor (KNN), and eXtreme gradient boosting (XGBoost), the tenfold cross-validation shows that the determination coefficient (R2) of our suggested DF model is improved by 10.4%–74.2%. Moreover, the parameter \(\Phi\) obtained by the SHapley Additive exPlanations (SHAP) method analysis can be used to guide the design and development of BMGs. Finally, a design and development of scheme process for BMGs that meets the expected requirements is given via parameter \(\Phi\) and the constructed DF model.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 51971188).

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Authors and Affiliations

  1. School of Mechanical Engineering and Mechanics, Xiangtan University, Xiangtan, 411105, Hunan, China

    Tao Long

  2. School of Civil Engineering, Xiangtan University, Xiangtan, 411105, Hunan, China

    Zhilin Long

  3. School of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, Hunan, China

    Zheng Peng

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  1. Tao Long
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  2. Zhilin Long
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  3. Zheng Peng
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Contributions

TL was responsible for conceptualization, visualization, investigation, data curation, formal analysis, and writing and preparing the original draft. ZL was responsible for conceptualization and writing—review, editing, and supervision. ZP was responsible for writing—review and editing.

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Correspondence to Zhilin Long.

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Long, T., Long, Z. & Peng, Z. Rational design and glass-forming ability prediction of bulk metallic glasses via interpretable machine learning. J Mater Sci 58, 8833–8844 (2023). https://doi.org/10.1007/s10853-023-08528-x

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